Toward Real-Time Mirrors Intelligence: System-Level Latency and Computation Evaluation in Internet of Mirrors (IoM)
This paper addresses the critical design challenge of computation placement in the Internet of Mirrors (IoM) ecosystem, which directly affects end-to-end latency, resource utilisation, and user experience for IoM users.
The Internet of Mirrors (IoM) is an IoT ecosystem of smart mirrors. This paper evaluates four computational placement strategies across its three-tier hierarchy under real Wi-Fi and 5G conditions, finding that offloading classification to higher-tier nodes reduces latency and consumer resource load, but introduces network overhead that scales with payload size and hop count.
The Internet of Mirrors (IoM) is an emerging IoT ecosystem of interconnected smart mirrors designed to deliver personalised services across a three-tier node hierarchy spanning consumer, professional, and hub nodes. Determining where computation should reside within this hierarchy is a critical design challenge, as placement decisions directly affect end-to-end latency, resource utilisation, and user experience. This paper presents the first physical IoM testbed study, evaluating four computational placement strategies across the IoM tier hierarchy under real Wi-Fi and 5G network conditions. Results show that offloading classification to higher-tier nodes substantially reduces latency and consumer resource load, but introduces network overhead that scales with payload size and hop count. No single strategy is universally optimal: the best choice depends on available network, node proximity, and concurrent user load. These findings empirically characterise the computation-communication trade-off space of the IoM and motivate the need for intelligent, adaptive task placement responsive to application requirements and live ecosystem conditions.